Domain Knowledge in A*-Based Causal Discovery

• URL: http://arxiv.org/abs/2208.08247v1
• Date: Wed, 17 Aug 2022 11:56:37 GMT
• Title: Domain Knowledge in A*-Based Causal Discovery
• Abstract summary: Causal discovery has become a vital tool for scientists and practitioners wanting to discover causal relationships from observational data. Recent work has incorporated domain knowledge into constraint-based causal discovery. We focus on efficiently integrating several types of domain knowledge into A*-based causal discovery.
• Score: 6.510061176722249
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Causal discovery has become a vital tool for scientists and practitioners wanting to discover causal relationships from observational data. While most previous approaches to causal discovery have implicitly assumed that no expert domain knowledge is available, practitioners can often provide such domain knowledge from prior experience. Recent work has incorporated domain knowledge into constraint-based causal discovery. The majority of such constraint-based methods, however, assume causal faithfulness, which has been shown to be frequently violated in practice. Consequently, there has been renewed attention towards exact-search score-based causal discovery methods, which do not assume causal faithfulness, such as A*-based methods. However, there has been no consideration of these methods in the context of domain knowledge. In this work, we focus on efficiently integrating several types of domain knowledge into A*-based causal discovery. In doing so, we discuss and explain how domain knowledge can reduce the graph search space and then provide an analysis of the potential computational gains. We support these findings with experiments on synthetic and real data, showing that even small amounts of domain knowledge can dramatically speed up A*-based causal discovery and improve its performance and practicality.

Related papers

• Integrating Background Knowledge for Scalable Causal Discovery [7.542220697870242]
  Expert background knowledge is often available in practical applications of causal discovery.<n>Background knowledge can help causal discovery in terms of identifiability of causal effects and accuracy of the learned structure.
  arXiv  Detail & Related papers  (2026-07-11T19:40:30Z)
• Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting [95.24017293537025]
  Causal Ensemble Agent (CEA) is a novel framework that aggregates structural insights from statistical discovery experts across different graph levels.<n>CEA achieves the strongest overall performance across a wide range of causal discovery methods.
  arXiv  Detail & Related papers  (2026-06-09T09:09:07Z)
• Use What You Know: Causal Foundation Models with Partial Graphs [97.91863420927866]
  Recently proposed Causal Foundation Models (CFMs) promise a more unified approach by amortising causal discovery and inference in a single step.<n>We bridge this gap by introducing methods to condition CFMs on causal information, such as the causal graph or more readily available ancestral information.
  arXiv  Detail & Related papers  (2026-02-16T17:56:37Z)
• Class-aware Domain Knowledge Fusion and Fission for Continual Test-Time Adaptation [50.831196928686104]
  We propose a class-aware domain Knowledge Fusion and Fission method for continual test-time adaptation.<n>A domain Knowledge FIssion (KFI) module is designed to adaptively separate new domain knowledge from a paired class-aware domain prompt pool.<n>A domain Knowledge FUsion (KFU) module is further designed to merge the fissioned new knowledge into the existing knowledge pool with minimal cost.
  arXiv  Detail & Related papers  (2025-10-14T05:09:50Z)
• Can Large Language Models Help Experimental Design for Causal Discovery? [94.66802142727883]
  Large Language Model Guided Intervention Targeting (LeGIT) is a robust framework that effectively incorporates LLMs to augment existing numerical approaches for the intervention targeting in causal discovery. LeGIT demonstrates significant improvements and robustness over existing methods and even surpasses humans.
  arXiv  Detail & Related papers  (2025-03-03T03:43:05Z)
• Learning to Defer for Causal Discovery with Imperfect Experts [59.071731337922664]
  We propose L2D-CD, a method for gauging the correctness of expert recommendations and optimally combining them with data-driven causal discovery results. We evaluate L2D-CD on the canonical T"ubingen pairs dataset and demonstrate its superior performance compared to both the causal discovery method and the expert used in isolation.
  arXiv  Detail & Related papers  (2025-02-18T18:55:53Z)
• RADICE: Causal Graph Based Root Cause Analysis for System Performance Diagnostic [3.708415881042821]
  Root cause analysis is one of the most crucial operations in software reliability regarding system performance diagnostic. We present a novel causal domain knowledge model representing causal relations about the underlying system components. We then introduce RADICE, an algorithm that through the causal graph discovery, enhancement, refinement, and subtraction processes is able to output a root cause causal sub-graph.
  arXiv  Detail & Related papers  (2025-01-20T15:36:39Z)
• Unsupervised Pairwise Causal Discovery on Heterogeneous Data using Mutual Information Measures [49.1574468325115]
  Causal Discovery is a technique that tackles the challenge by analyzing the statistical properties of the constituent variables. We question the current (possibly misleading) baseline results on the basis that they were obtained through supervised learning. In consequence, we approach this problem in an unsupervised way, using robust Mutual Information measures.
  arXiv  Detail & Related papers  (2024-08-01T09:11:08Z)
• A Survey on Causal Discovery: Theory and Practice [2.741266294612776]
  Causal inference is designed to quantify the underlying relationships that connect a cause to its effect. In this paper, we explore recent advancements in a unified manner, provide a consistent overview of existing algorithms, report useful tools and data, present real-world applications.
  arXiv  Detail & Related papers  (2023-05-17T08:18:56Z)
• Optimizing Data-driven Causal Discovery Using Knowledge-guided Search [3.7489744097107316]
  This study introduces a knowledge-guided causal structure search (KGS) approach that utilizes observational data and structural priors as constraints to learn the causal graph. We extensively evaluate KGS in multiple settings using synthetic and benchmark real-world datasets, as well as in a real-life healthcare application related to oxygen therapy treatment.
  arXiv  Detail & Related papers  (2023-04-11T20:56:33Z)
• DOMINO: Visual Causal Reasoning with Time-Dependent Phenomena [59.291745595756346]
  We propose a set of visual analytics methods that allow humans to participate in the discovery of causal relations associated with windows of time delay. Specifically, we leverage a well-established method, logic-based causality, to enable analysts to test the significance of potential causes. Since an effect can be a cause of other effects, we allow users to aggregate different temporal cause-effect relations found with our method into a visual flow diagram.
  arXiv  Detail & Related papers  (2023-03-12T03:40:21Z)
• Learning domain-specific causal discovery from time series [7.298647409503783]
  Causal discovery from time-varying data is important in neuroscience, medicine, and machine learning. Human expertise is often not entirely accurate and tends to be outperformed in domains with abundant data. In this study, we examine whether we can enhance domain-specific causal discovery for time series using a data-driven approach.
  arXiv  Detail & Related papers  (2022-09-12T20:32:39Z)
• Valid Inference After Causal Discovery [73.87055989355737]
  We develop tools for valid post-causal-discovery inference. We show that a naive combination of causal discovery and subsequent inference algorithms leads to highly inflated miscoverage rates.
  arXiv  Detail & Related papers  (2022-08-11T17:40:45Z)
• BaCaDI: Bayesian Causal Discovery with Unknown Interventions [118.93754590721173]
  BaCaDI operates in the continuous space of latent probabilistic representations of both causal structures and interventions. In experiments on synthetic causal discovery tasks and simulated gene-expression data, BaCaDI outperforms related methods in identifying causal structures and intervention targets.
  arXiv  Detail & Related papers  (2022-06-03T16:25:48Z)
• Principled Knowledge Extrapolation with GANs [92.62635018136476]
  We study counterfactual synthesis from a new perspective of knowledge extrapolation. We show that an adversarial game with a closed-form discriminator can be used to address the knowledge extrapolation problem. Our method enjoys both elegant theoretical guarantees and superior performance in many scenarios.
  arXiv  Detail & Related papers  (2022-05-21T08:39:42Z)
• To do or not to do: finding causal relations in smart homes [2.064612766965483]
  This paper introduces a new way to learn causal models from a mixture of experiments on the environment and observational data. The core of our method is the use of selected interventions, especially our learning takes into account the variables where it is impossible to intervene. We use our method on a smart home simulation, a use case where knowing causal relations pave the way towards explainable systems.
  arXiv  Detail & Related papers  (2021-05-20T22:36:04Z)
• Selecting Data Augmentation for Simulating Interventions [12.848239550098693]
  Machine learning models trained with purely observational data and the principle of empirical risk fail to generalize to unseen domains. We argue that causal concepts can be used to explain the success of data augmentation by describing how they can weaken the spurious correlation between the observed domains and the task labels.
  arXiv  Detail & Related papers  (2020-05-04T21:33:29Z)
• Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills [155.11646755470582]
  'Explore, Discover and Learn' (EDL) is an alternative approach to information-theoretic skill discovery. We show that EDL offers significant advantages, such as overcoming the coverage problem, reducing the dependence of learned skills on the initial state, and allowing the user to define a prior over which behaviors should be learned.
  arXiv  Detail & Related papers  (2020-02-10T10:49:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.